Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.
Ontology highlight
ABSTRACT:
SUBMITTER: Javaid A
PROVIDER: S-EPMC9460561 | biostudies-literature | 2022 Dec
REPOSITORIES: biostudies-literature
Publications
Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.
Javaid Aamir A Zghyer Fawzi F Kim Chang C Spaulding Erin M EM Isakadze Nino N Ding Jie J Kargillis Daniel D Gao Yumin Y Rahman Faisal F Brown Donald E DE Saria Suchi S Martin Seth S SS Kramer Christopher M CM Blumenthal Roger S RS Marvel Francoise A FA
American journal of preventive cardiology 20220829
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions mu ...[more]
Similar Datasets
Project description:Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
| S-EPMC7490367 | biostudies-literature
Project description:BackgroundCardiovascular disease (CVD) is a major global health issue, with approximately 70% of cases linked to modifiable risk factors. Digital health solutions offer potential for CVD prevention; yet, their effectiveness in covering the full range of prevention strategies is uncertain.ObjectiveThis study aimed to synthesize current literature on digital solutions for CVD prevention, identify the key components of effective digital interventions, and highlight critical research gaps to inform the development of sustainable strategies for CVD prevention.MethodsFollowing PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a comprehensive search in Web of Science, Scopus, and PubMed to identify original English-language studies published between January 2000 and May 2024 that examined primary or secondary CVD prevention through digital solutions. The exclusion criteria included: telephone-only interventions, abstract-only publications, methodology-focused studies without primary data, studies without participants or specific groups, and studies with no follow-up period. The literature search used the string with terms like "digital health," "mHealth," "mobile health," "text message," "short message service," "SMS," "prevention," "prevent," "cardiovascular disease," "CVD," etc. Study bias was assessed using the RoB 2 (Cochrane Collaboration) and the ROBINS-I tool (Cochrane Collaboration). Data on prevention components, prevention types, study design, population, intervention, follow-up duration, personnel, and delivery settings were extracted.ResultsA total of 2871 studies were identified through the search. After excluding ineligible studies, 30 studies remained, including 24 randomized controlled trials. The reviewed digital solutions for CVD prevention focused on baseline assessment (29/30, 97%), physical activity counseling (18/30, 60%), tobacco cessation (14/30, 47%), blood pressure management (13/30, 43%), and medication adherence (10/30, 33%). The technologies used were categorized into 3 types, smartphones and wearables (16/30, 53%), email and SMS communications (12/30, 40%), and websites or web portals (3/30, 10%). The majority of the study outcomes addressed blood pressure (14/30, 47%), exercise capacity (12/30, 40%), weight (12/30, 40%), and lipid profile (11/30, 37%), while fewer focused on nicotine dependence (9/30, 30%), medication use (8/30, 27%), quality of life (7/30, 23%), dietary habits (5/30, 17%), intervention adherence (4/30, 13%), waist circumference (4/30, 13%), and blood glucose levels (2/30, 7%).ConclusionsDigital solutions can address challenges in traditional CVD prevention by improving preventive behaviors and monitoring health indicators. However, most evaluated interventions have focused on medication use, quality of life, dietary habits, adherence, and waist circumference. Further studies are needed to assess the long-term impact of more comprehensive interventions on key cardiovascular outcomes.
| S-EPMC11803337 | biostudies-literature
Project description:ObjectivePrecision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies.MethodsWe performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research.ResultsWe identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and 'omics'-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects.DiscussionInnovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.
| S-EPMC6244622 | biostudies-literature
Project description:Cardiovascular disease is the leading cause of death in the United States. While abundant research has been conducted to identify risk factors for cardiovascular disease at the individual level, less is known about factors that may influence population cardiovascular health outcomes at the neighborhood level. The purpose of this study is to use Bayesian Additive Regression Trees, a state-of-the-art machine learning approach, to rank sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health. We created a new neighborhood health dataset by combining three datasets at the census tract level, including the 500 Cities Data from the Centers for Disease Control and Prevention, the 2011-2015 American Community Survey 5-Year Estimates from the Census Bureau, and the 2015-2016 Environmental Justice Screening database from the Environmental Protection Agency in the United States. Results showed that neighborhood behavioral factors such as the proportions of people who are obese, do not have leisure-time physical activity, and have binge drinking emerged as top five predictors for most of the neighborhood cardiovascular health outcomes. Findings from this study would allow public health researchers and policymakers to prioritize community-based interventions and efficiently use limited resources to improve neighborhood cardiovascular health.
| S-EPMC7704682 | biostudies-literature
Project description:Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide yet the majority of related risk factors are largely preventable (primary prevention [PP]) and effectively treatable (secondary prevention [SP]) with healthy lifestyle behaviors. The use of information and communication technology (ICT) offers a unique approach to personal health and CVD prevention, as these mediums are relatively affordable, approachable, and accessible. The purpose of this review is to provide an overview of ICT-driven personal health technologies and their potential role in promoting and supporting self-care behaviors for PP and SP of CVD. In this review, we focus on technological interventions that have been successful at supporting positive behavior change in order to determine which tools, resources, and methods are most appropriate for delivering interventions geared towards CVD prevention. We conducted a literature search from a range of sources including scholarly, peer-reviewed journal articles indexed in PubMed and CINAHL, gray literature, and reputable websites and other Internet-based media. A synthesis of existing literature indicates that the overall efficacy of ICT-driven personal health technologies is largely determined by: 1) the educational resources provided and the extent to which the relayed information is customized or individually tailored; and 2) the degree of self-monitoring and levels of personalized feedback or other interactions (e.g. interpersonal communications). We conclude that virtually all the technological tools and resources identified (e.g. Internet-based communications including websites, weblogs and wikis, mobile devices and applications, social media, and wearable monitors) can be strategically leveraged to enhance self-care behaviors for CVD risk reduction and SP but further research is needed to evaluate their efficacy, cost-effectiveness, and long-term maintainability.
| S-EPMC4633704 | biostudies-literature
Project description:Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.
| S-EPMC11524492 | biostudies-literature
Project description:Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.
| S-EPMC6614991 | biostudies-other
Project description:Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
| S-EPMC5831252 | biostudies-literature
Project description:IntroductionThe use of health information technologies (HITs) has been associated with positive benefits such as improved health outcomes and improved health services. Results from empirical studies reported potential benefits of HITs in preventive medicine measures such as primary prevention. This review will examine the broad range of HITs and their uses and effectiveness in primary prevention.Methods and analysisWe will conduct searches in relevant databases (MEDLINE, EMBASE, the Cochrane Methodology Register, Cochrane Database of Systematic Reviews, CINAHL, SCOPUS and Web of Science) using Arksey and O'Malley's scoping review methodology. The scoping review will include all study designs to identify the literature on HIT uses. Two reviewers will independently screen the literature following our screening criteria and using a data abstraction form. Findings will be summarised quantitatively (using numerical counts of HITs) and qualitatively (using narrative synthesis).Ethics and disseminationThe study will synthesise data from published literature and will not require an ethical approval. The results of the review will be disseminated through a peer-reviewed journal.
| S-EPMC6173258 | biostudies-literature
Project description:With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.
| S-EPMC5556681 | biostudies-other